GeeLytics: Geo-distributed edge analytics for large scale IoT systems based on dynamic topology

High data rate sensors such as video cameras, audio sensors, and motion sensors are becoming ubiquitous in the Internet of Things (IoT). In large scale IoT systems like smart cities, a large number of sensors are now widely deployed at different locations, generating a huge amount of stream data. Although the generated data provide us great potential to sense our live environments, it still remains a big challenge to efficiently extract real-time results from sensor data to make fast decisions. Existing stream processing platforms, such as Storm, Spark Streaming, and S4, are well designed to process stream data within a cluster in the Cloud, but they are not suitable for highly distributed IoT systems in which data are naturally geo-distributed and low latency analytics results are expected to be shared across users and applications. To tackle this problem, we design an edge analytics platform called GeeLytics, which can perform real-time stream processing both at the network edges and in the Cloud in a dynamic and transparent manner. In this position paper we discuss its use cases, motivation, and preliminary architecture design. As compared with the start of the art, GeeLytics is designed to support dynamic stream processing topologies by taking into account the system characteristics of heterogeneous edge/Cloud nodes and also the current system workload. This shall achieve low latency analytics results while minimizing the edge-to-Cloud bandwidth consumption. In addition, using docker application containers for packaging up deployable tasks and a distributed pub/sub mechanism for inter-task stream data routing, GeeLytics shall provide better resource isolation and system efficiency to support multi-tenancy.